Improving Metrical Grammar with Grammar Expansion

This paper describes Metrical PCFG Model that represents the metrical structure of music by its derivation. Because the basic grammar of Metrical PCFG model is too simple, we also propose a grammar expansion method that improves the grammar by duplicating a nonterminal symbol and its rules. At first...

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Bibliographic Details
Published inAI 2008: Advances in Artificial Intelligence pp. 180 - 191
Main Authors Tanji, Makoto, Ando, Daichi, Iba, Hitoshi
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Metrical Structure
Note Sequence
Parse Tree
Production Rule
Rhythmic Pattern
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Summary:This paper describes Metrical PCFG Model that represents the metrical structure of music by its derivation. Because the basic grammar of Metrical PCFG model is too simple, we also propose a grammar expansion method that improves the grammar by duplicating a nonterminal symbol and its rules. At first, a simple PCFG model which that represent the metrical structure by a derivation just like parse tree in natural language processing. The grammar expansion operator duplicates a symbol and its rules in the PCFG model. Then the parameters of PCFG are estimated by EM algorithm. We conducted two experiments. The first one shows the expansion method specialized symbols and rules to adapt to the training data. Rhythmic patterns in a piece were represented by expanded symbols. And in the second experiment, we investigated how the expansion method improves performance of prediction for new pieces with large corpus.
ISBN:9783540893776
3540893776
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-540-89378-3_18